基于RSSI的无线传感器网络定位算法的研究与实现

发布时间：2018-07-20 15:45

【摘要】：随着计算机技术、通信技术以及嵌入式系统的迅速发展,无线传感器网络(WSN)已成为一个重要的研究领域。WSN是把大量的传感器节点随机地播撒在监测区域内,通过自动组网的方式形成智能网络系统来对目标进行监测,之后对监测的数据进行处理并传送到管理用户。节点定位技术是WSN进行监控、预报和识别等应用技术的基础,在整个网络中通常选取少部分节点带有自定位的GPS模块并将它们视为锚节点,而待测节点则采用自组能力强、连通性好、定位精度高的定位算法来实现节点定位。本文主要研究了依赖测距的定位算法和非依赖测距的定位算法。其中依赖测距的定位算法主要是测量各节点间的角度或距离等信息,定位精度相对来说较高,而且能够满足很多应用的需求。因此对基于接收信号强度指示(RSSI)的无线传感器网络定位算法进行深入研究,由于该定位算法在节点的测距和定位过程中都存在较大误差,因此本文对该算法的误差进行了改进。改进的节点定位算法首先是通过建立Kalman滤波器模型并利用其对通信节点的RSSI信号值进行平滑处理,使待测节点接收到的信号强度值更趋近于真实值;然后通过选取待测节点和与其相邻的各锚节点之间距离倒数的和作为权值因子,同时利用通信节点间的测量距离对权值因子加以修正,再利用二次加权质心定位算法定位待测节点的位置坐标;最后再通过定位距待测节点相近的锚节点来得出定位均差,对待测节点的坐标进行误差补偿。通过仿真实验,本文提出的改进定位算法比基于RSSI的加权质心算法提高了5%-6%的定位精度。
[Abstract]:With the rapid development of computer technology, communication technology and embedded system, wireless sensor network (WSN) has become an important research field. WSN is to spread a large number of sensor nodes randomly in the monitoring area. An intelligent network system is formed by automatic networking to monitor the target, and then the monitored data are processed and transmitted to the management user. Node location technology is the basis of WSN monitoring, prediction and identification technology. In the whole network, a few nodes with self-positioning GPS modules are usually selected and regarded as anchor nodes, while the nodes under test have strong self-organization ability. The location algorithm with good connectivity and high positioning accuracy is used to realize node localization. In this paper, we mainly study the location algorithm based on distance measurement and the location algorithm of non-dependent ranging. The localization algorithm based on ranging is mainly used to measure the angle or distance between the nodes. The location accuracy is relatively high and can meet the needs of many applications. Therefore, the location algorithm of wireless sensor networks based on received signal intensity indication (RSSI) is deeply studied. Because of the large errors in the location and location process of nodes, the error of the algorithm is improved in this paper. The improved node localization algorithm firstly establishes the Kalman filter model and uses it to smooth the RSSI signal value of the communication node so that the signal intensity value of the node under test is closer to the real value. Then the reciprocal sum of the distance between the nodes to be tested and the adjacent anchor nodes is selected as the weight factor. At the same time, the weight factor is modified by the measurement distance between the communication nodes. The second weighted centroid positioning algorithm is used to locate the coordinates of the nodes to be tested. Finally, the location mean difference is obtained by locating the anchor nodes which are close to the measured nodes, and the errors of the measured nodes are compensated. Through simulation experiments, the improved localization algorithm proposed in this paper improves the positioning accuracy by 5- 6% compared with the weighted centroid algorithm based on RSSI.
【学位授予单位】：沈阳航空航天大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP212.9;TN929.5

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